SimulatedAnnealingLHS¶
- class SimulatedAnnealingLHS(*args)¶
LHS optimization using simulated annealing.
Performs the optimization of an LHS using simulated annealing algorithm.
Available constructors:
SimulatedAnnealingLHS(lhsDesign)
SimulatedAnnealingLHS(lhsDesign, spaceFilling)
SimulatedAnnealingLHS(lhsDesign, spaceFilling, profile)
SimulatedAnnealingLHS(initialDesign, distribution)
SimulatedAnnealingLHS(initialDesign, distribution, spaceFilling)
SimulatedAnnealingLHS(initialDesign, distribution, spaceFilling, profile)
- Parameters:
- lhsDesign
LHSExperiment
Factory that generate designs
- initialDesign2d-array sequence
Initial design to be optimized
- distribution
Distribution
Distribution of designs
- spaceFilling
SpaceFilling
Criterion to be optimized Default is
SpaceFillingPhiP
- profile
TemperatureProfile
Temperature profile used by the simulated annealing algorithm Default is
GeometricProfile
- lhsDesign
Notes
With the first constructor, the initial design is generated thanks to lhsDesign. With the second usage (initialDesign), it must be already generated. Starting from this design, a new design is obtained by swapping one random coordinate of two randomly chosen elements; by construction, this design is also an LHS design. If the new design is better than the previous one, it is kept. If it is worse, it may anyway be kept with some probability, which depends on how these designs compare, but also on a temperature profile T which decreases over time. This means that jumping away from local extrema becomes less probable over time.
Examples
>>> import openturns as ot >>> dimension = 3 >>> size = 100 >>> # Build standard randomized LHS algorithm >>> distribution = ot.JointDistribution([ot.Uniform(0.0, 1.0)]*dimension) >>> lhs = ot.LHSExperiment(distribution, size) >>> lhs.setAlwaysShuffle(True) # randomized >>> # Defining space fillings >>> spaceFilling = ot.SpaceFillingC2() >>> # Geometric profile >>> geomProfile = ot.GeometricProfile(10.0, 0.95, 2000) >>> # Simulated Annealing LHS with geometric temperature profile, C2 optimization >>> optimalLHSAlgorithm = ot.SimulatedAnnealingLHS(lhs, spaceFilling, geomProfile)
Methods
generate
()Generate points according to the type of the experiment.
generateWithRestart
(nRestart)Generate sample with restart.
Generate points and their associated weight according to the type of the experiment.
Accessor to the object's name.
Accessor to the distribution.
getLHS
()Return the LHS design.
getName
()Accessor to the object's name.
Result accessor.
getSize
()Accessor to the size of the generated sample.
Return the space-filling criterion to be optimized.
hasName
()Test if the object is named.
Ask whether the experiment has uniform weights.
isRandom
()Accessor to the randomness of quadrature.
setDistribution
(distribution)Accessor to the distribution.
setName
(name)Accessor to the object's name.
setSize
(size)Accessor to the size of the generated sample.
- __init__(*args)¶
- generate()¶
Generate points according to the type of the experiment.
- Returns:
- sample
Sample
Points of the design of experiments. The sampling method is defined by the type of the weighted experiment.
- sample
Examples
>>> import openturns as ot >>> ot.RandomGenerator.SetSeed(0) >>> myExperiment = ot.MonteCarloExperiment(ot.Normal(2), 5) >>> sample = myExperiment.generate() >>> print(sample) [ X0 X1 ] 0 : [ 0.608202 -1.26617 ] 1 : [ -0.438266 1.20548 ] 2 : [ -2.18139 0.350042 ] 3 : [ -0.355007 1.43725 ] 4 : [ 0.810668 0.793156 ]
- generateWithRestart(nRestart)¶
Generate sample with restart.
Randomly generate several samples from an initial state and select the one that has the best score.
- Parameters:
- nRestartint
Number of restarts
- Returns:
- sample
Sample
The best scored sample across restarts
- sample
- generateWithWeights()¶
Generate points and their associated weight according to the type of the experiment.
- Returns:
Examples
>>> import openturns as ot >>> ot.RandomGenerator.SetSeed(0) >>> myExperiment = ot.MonteCarloExperiment(ot.Normal(2), 5) >>> sample, weights = myExperiment.generateWithWeights() >>> print(sample) [ X0 X1 ] 0 : [ 0.608202 -1.26617 ] 1 : [ -0.438266 1.20548 ] 2 : [ -2.18139 0.350042 ] 3 : [ -0.355007 1.43725 ] 4 : [ 0.810668 0.793156 ] >>> print(weights) [0.2,0.2,0.2,0.2,0.2]
- getClassName()¶
Accessor to the object’s name.
- Returns:
- class_namestr
The object class name (object.__class__.__name__).
- getDistribution()¶
Accessor to the distribution.
- Returns:
- distribution
Distribution
Distribution of the input random vector.
- distribution
- getLHS()¶
Return the LHS design.
- Returns:
- value
LHSExperiment
Result the factory that builds initial design to be optimized
- value
- getName()¶
Accessor to the object’s name.
- Returns:
- namestr
The name of the object.
- getResult()¶
Result accessor.
- Returns:
- value
LHSResult
Result of generation that contains the optimal design, some criteria and history
- value
- getSize()¶
Accessor to the size of the generated sample.
- Returns:
- sizepositive int
Number of points constituting the design of experiments.
- getSpaceFilling()¶
Return the space-filling criterion to be optimized.
- Returns:
- value
SpaceFilling
Criterion function to be optimized
- value
- hasName()¶
Test if the object is named.
- Returns:
- hasNamebool
True if the name is not empty.
- hasUniformWeights()¶
Ask whether the experiment has uniform weights.
- Returns:
- hasUniformWeightsbool
Whether the experiment has uniform weights.
- isRandom()¶
Accessor to the randomness of quadrature.
- Parameters:
- isRandombool
Is true if the design of experiments is random. Otherwise, the design of experiment is assumed to be deterministic.
- setDistribution(distribution)¶
Accessor to the distribution.
- Parameters:
- distribution
Distribution
Distribution of the input random vector.
- distribution
- setName(name)¶
Accessor to the object’s name.
- Parameters:
- namestr
The name of the object.
- setSize(size)¶
Accessor to the size of the generated sample.
- Parameters:
- sizepositive int
Number of points constituting the design of experiments.
Examples using the class¶
Kriging: configure the optimization solver
Optimize an LHS design of experiments